Aging Intensity for Step-Stress Accelerated Life Testing Experiments

Abstract

The aging intensity (AI), defined as the ratio of the instantaneous hazard rate and a baseline hazard rate, is a useful tool for the describing reliability properties of a random variable corresponding to a lifetime. In this work, the concept of AI is introduced in step-stress accelerated life testing (SSALT) experiments, providing new insights to the model and enabling the further clarification of the differences between the two commonly employed cumulative exposure (CE) and tampered failure rate (TFR) models. New AI-based estimators for the parameters of a SSALT model are proposed and compared to the MLEs in terms of examples and a simulation study.

Keywords

• cumulative exposure
• cumulative Kulback–Leibler divergence
• exponential distribution
• kernel density estimation
• Kulback–Leibler divergence
• maximum likelihood estimation